Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations100000
Missing cells200
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory65.8 MiB
Average record size in memory690.1 B

Variable types

Text2
Categorical13
Numeric14
DateTime1
Unsupported1

Alerts

avg_monthly_balance is highly overall correlated with pca_1 and 1 other fieldsHigh correlation
cluster is highly overall correlated with cluster_labelHigh correlation
cluster_label is highly overall correlated with clusterHigh correlation
credit_to_income is highly overall correlated with income and 5 other fieldsHigh correlation
income is highly overall correlated with credit_to_income and 4 other fieldsHigh correlation
income_credit_interaction is highly overall correlated with credit_to_income and 4 other fieldsHigh correlation
income_log is highly overall correlated with credit_to_income and 4 other fieldsHigh correlation
loan_amount is highly overall correlated with credit_to_income and 2 other fieldsHigh correlation
loan_approved is highly overall correlated with potential_data_leakageHigh correlation
loan_log is highly overall correlated with credit_to_income and 2 other fieldsHigh correlation
pca_1 is highly overall correlated with avg_monthly_balance and 3 other fieldsHigh correlation
pca_2 is highly overall correlated with credit_to_income and 5 other fieldsHigh correlation
potential_data_leakage is highly overall correlated with loan_approvedHigh correlation
transaction_amount is highly overall correlated with transaction_typeHigh correlation
transaction_type is highly overall correlated with transaction_amountHigh correlation
txn_intensity is highly overall correlated with avg_monthly_balanceHigh correlation
credit_to_income is highly skewed (γ1 = 49.72325454) Skewed
txn_intensity is highly skewed (γ1 = -25.32234104) Skewed
month is an unsupported type, check if it needs cleaning or further analysis Unsupported
loan_amount has 3000 (3.0%) zeros Zeros
loan_log has 3000 (3.0%) zeros Zeros
credit_to_income has 3000 (3.0%) zeros Zeros

Reproduction

Analysis started2025-04-14 16:48:51.608993
Analysis finished2025-04-14 16:49:12.589397
Duration20.98 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct2500
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
2025-04-14T22:19:12.852362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters900000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCUST00001
2nd rowCUST00001
3rd rowCUST00001
4th rowCUST00001
5th rowCUST00001
ValueCountFrequency (%)
cust00001 40
 
< 0.1%
cust00020 40
 
< 0.1%
cust00039 40
 
< 0.1%
cust00008 40
 
< 0.1%
cust00010 40
 
< 0.1%
cust00012 40
 
< 0.1%
cust00014 40
 
< 0.1%
cust00017 40
 
< 0.1%
cust00019 40
 
< 0.1%
cust00023 40
 
< 0.1%
Other values (2490) 99600
99.6%
2025-04-14T22:19:13.228583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 141600
15.7%
C 100000
11.1%
U 100000
11.1%
S 100000
11.1%
T 100000
11.1%
1 41560
 
4.6%
4 41440
 
4.6%
3 40640
 
4.5%
8 40640
 
4.5%
7 40560
 
4.5%
Other values (4) 153560
17.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 900000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 141600
15.7%
C 100000
11.1%
U 100000
11.1%
S 100000
11.1%
T 100000
11.1%
1 41560
 
4.6%
4 41440
 
4.6%
3 40640
 
4.5%
8 40640
 
4.5%
7 40560
 
4.5%
Other values (4) 153560
17.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 900000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 141600
15.7%
C 100000
11.1%
U 100000
11.1%
S 100000
11.1%
T 100000
11.1%
1 41560
 
4.6%
4 41440
 
4.6%
3 40640
 
4.5%
8 40640
 
4.5%
7 40560
 
4.5%
Other values (4) 153560
17.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 900000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 141600
15.7%
C 100000
11.1%
U 100000
11.1%
S 100000
11.1%
T 100000
11.1%
1 41560
 
4.6%
4 41440
 
4.6%
3 40640
 
4.5%
8 40640
 
4.5%
7 40560
 
4.5%
Other values (4) 153560
17.1%

account_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
Checking
33960 
Salary
33600 
Savings
32440 

Length

Max length8
Median length7
Mean length7.0036
Min length6

Characters and Unicode

Total characters700360
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChecking
2nd rowChecking
3rd rowChecking
4th rowChecking
5th rowChecking

Common Values

ValueCountFrequency (%)
Checking 33960
34.0%
Salary 33600
33.6%
Savings 32440
32.4%

Length

2025-04-14T22:19:13.332362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:19:13.404251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
checking 33960
34.0%
salary 33600
33.6%
savings 32440
32.4%

Most occurring characters

ValueCountFrequency (%)
a 99640
14.2%
i 66400
9.5%
n 66400
9.5%
g 66400
9.5%
S 66040
 
9.4%
C 33960
 
4.8%
h 33960
 
4.8%
e 33960
 
4.8%
c 33960
 
4.8%
k 33960
 
4.8%
Other values (5) 165680
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 700360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 99640
14.2%
i 66400
9.5%
n 66400
9.5%
g 66400
9.5%
S 66040
 
9.4%
C 33960
 
4.8%
h 33960
 
4.8%
e 33960
 
4.8%
c 33960
 
4.8%
k 33960
 
4.8%
Other values (5) 165680
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 700360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 99640
14.2%
i 66400
9.5%
n 66400
9.5%
g 66400
9.5%
S 66040
 
9.4%
C 33960
 
4.8%
h 33960
 
4.8%
e 33960
 
4.8%
c 33960
 
4.8%
k 33960
 
4.8%
Other values (5) 165680
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 700360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 99640
14.2%
i 66400
9.5%
n 66400
9.5%
g 66400
9.5%
S 66040
 
9.4%
C 33960
 
4.8%
h 33960
 
4.8%
e 33960
 
4.8%
c 33960
 
4.8%
k 33960
 
4.8%
Other values (5) 165680
23.7%

income
Real number (ℝ)

High correlation 

Distinct2375
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49958.179
Minimum-10969.05
Maximum106662.35
Zeros0
Zeros (%)0.0%
Negative200
Negative (%)0.2%
Memory size781.4 KiB
2025-04-14T22:19:13.480512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-10969.05
5-th percentile25439.431
Q140262.615
median50074.445
Q360051.6
95-th percentile74269.95
Maximum106662.35
Range117631.4
Interquartile range (IQR)19788.985

Descriptive statistics

Standard deviation14973.502
Coefficient of variation (CV)0.29972073
Kurtosis0.35824293
Mean49958.179
Median Absolute Deviation (MAD)9900.69
Skewness-0.062855809
Sum4.9958179 × 109
Variance2.2420576 × 108
MonotonicityNot monotonic
2025-04-14T22:19:13.571673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50074.445 5040
 
5.0%
38985.86 40
 
< 0.1%
30401.1 40
 
< 0.1%
76327.72 40
 
< 0.1%
63600.74 40
 
< 0.1%
66584.91 40
 
< 0.1%
47304.1 40
 
< 0.1%
46787.36 40
 
< 0.1%
49572.1 40
 
< 0.1%
60738.07 40
 
< 0.1%
Other values (2365) 94600
94.6%
ValueCountFrequency (%)
-10969.05 40
< 0.1%
-10286.82 40
< 0.1%
-4293.06 40
< 0.1%
-1813.82 40
< 0.1%
-808.52 40
< 0.1%
38.21 40
< 0.1%
4735.3 40
< 0.1%
9790.83 40
< 0.1%
10187.56 40
< 0.1%
10293.32 40
< 0.1%
ValueCountFrequency (%)
106662.35 40
< 0.1%
103346.87 40
< 0.1%
102705.14 40
< 0.1%
98447.44 40
< 0.1%
98239.21 40
< 0.1%
97233.45 40
< 0.1%
96698.78 40
< 0.1%
94039.62 40
< 0.1%
93755.49 40
< 0.1%
93420.26 40
< 0.1%

credit_score
Real number (ℝ)

Distinct264
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean680.5408
Minimum517
Maximum855
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-04-14T22:19:13.658242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum517
5-th percentile604
Q1649
median680
Q3711
95-th percentile761.05
Maximum855
Range338
Interquartile range (IQR)62

Descriptive statistics

Standard deviation47.493398
Coefficient of variation (CV)0.069787731
Kurtosis0.20725817
Mean680.5408
Median Absolute Deviation (MAD)31
Skewness0.065335142
Sum68054080
Variance2255.6229
MonotonicityNot monotonic
2025-04-14T22:19:13.785401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
680 6200
 
6.2%
656 1200
 
1.2%
695 1080
 
1.1%
705 1000
 
1.0%
679 960
 
1.0%
706 920
 
0.9%
713 880
 
0.9%
711 880
 
0.9%
703 880
 
0.9%
660 880
 
0.9%
Other values (254) 85120
85.1%
ValueCountFrequency (%)
517 40
< 0.1%
525 40
< 0.1%
526 40
< 0.1%
532 40
< 0.1%
537 40
< 0.1%
539 40
< 0.1%
546 40
< 0.1%
550 40
< 0.1%
551 40
< 0.1%
552 80
0.1%
ValueCountFrequency (%)
855 40
< 0.1%
830 40
< 0.1%
828 40
< 0.1%
827 40
< 0.1%
826 40
< 0.1%
825 40
< 0.1%
824 40
< 0.1%
822 40
< 0.1%
820 40
< 0.1%
815 40
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
Self-employed
34600 
Employed
33160 
Unemployed
32240 

Length

Max length13
Median length10
Mean length10.3748
Min length8

Characters and Unicode

Total characters1037480
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelf-employed
2nd rowSelf-employed
3rd rowSelf-employed
4th rowSelf-employed
5th rowSelf-employed

Common Values

ValueCountFrequency (%)
Self-employed 34600
34.6%
Employed 33160
33.2%
Unemployed 32240
32.2%

Length

2025-04-14T22:19:13.872876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:19:13.937777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
self-employed 34600
34.6%
employed 33160
33.2%
unemployed 32240
32.2%

Most occurring characters

ValueCountFrequency (%)
e 201440
19.4%
l 134600
13.0%
m 100000
9.6%
p 100000
9.6%
o 100000
9.6%
y 100000
9.6%
d 100000
9.6%
S 34600
 
3.3%
f 34600
 
3.3%
- 34600
 
3.3%
Other values (3) 97640
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1037480
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 201440
19.4%
l 134600
13.0%
m 100000
9.6%
p 100000
9.6%
o 100000
9.6%
y 100000
9.6%
d 100000
9.6%
S 34600
 
3.3%
f 34600
 
3.3%
- 34600
 
3.3%
Other values (3) 97640
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1037480
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 201440
19.4%
l 134600
13.0%
m 100000
9.6%
p 100000
9.6%
o 100000
9.6%
y 100000
9.6%
d 100000
9.6%
S 34600
 
3.3%
f 34600
 
3.3%
- 34600
 
3.3%
Other values (3) 97640
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1037480
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 201440
19.4%
l 134600
13.0%
m 100000
9.6%
p 100000
9.6%
o 100000
9.6%
y 100000
9.6%
d 100000
9.6%
S 34600
 
3.3%
f 34600
 
3.3%
- 34600
 
3.3%
Other values (3) 97640
9.4%

risk_segment
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
Medium
33720 
Low
33520 
High
32760 

Length

Max length6
Median length4
Mean length4.3392
Min length3

Characters and Unicode

Total characters433920
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowMedium
4th rowMedium
5th rowMedium

Common Values

ValueCountFrequency (%)
Medium 33720
33.7%
Low 33520
33.5%
High 32760
32.8%

Length

2025-04-14T22:19:14.013438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:19:14.080143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
medium 33720
33.7%
low 33520
33.5%
high 32760
32.8%

Most occurring characters

ValueCountFrequency (%)
i 66480
15.3%
M 33720
7.8%
e 33720
7.8%
d 33720
7.8%
u 33720
7.8%
m 33720
7.8%
L 33520
7.7%
o 33520
7.7%
w 33520
7.7%
H 32760
7.5%
Other values (2) 65520
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 433920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 66480
15.3%
M 33720
7.8%
e 33720
7.8%
d 33720
7.8%
u 33720
7.8%
m 33720
7.8%
L 33520
7.7%
o 33520
7.7%
w 33520
7.7%
H 32760
7.5%
Other values (2) 65520
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 433920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 66480
15.3%
M 33720
7.8%
e 33720
7.8%
d 33720
7.8%
u 33720
7.8%
m 33720
7.8%
L 33520
7.7%
o 33520
7.7%
w 33520
7.7%
H 32760
7.5%
Other values (2) 65520
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 433920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 66480
15.3%
M 33720
7.8%
e 33720
7.8%
d 33720
7.8%
u 33720
7.8%
m 33720
7.8%
L 33520
7.7%
o 33520
7.7%
w 33520
7.7%
H 32760
7.5%
Other values (2) 65520
15.1%

avg_monthly_balance
Real number (ℝ)

High correlation 

Distinct2499
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20141.573
Minimum-15218.08
Maximum53373.82
Zeros0
Zeros (%)0.0%
Negative2160
Negative (%)2.2%
Memory size781.4 KiB
2025-04-14T22:19:14.152399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-15218.08
5-th percentile3979.89
Q113211.865
median19875.915
Q326853.403
95-th percentile36973.173
Maximum53373.82
Range68591.9
Interquartile range (IQR)13641.537

Descriptive statistics

Standard deviation10018.577
Coefficient of variation (CV)0.49740788
Kurtosis-0.056890561
Mean20141.573
Median Absolute Deviation (MAD)6829.245
Skewness0.043922871
Sum2.0141573 × 109
Variance1.0037189 × 108
MonotonicityNot monotonic
2025-04-14T22:19:14.240982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23395.37 80
 
0.1%
13080.97 40
 
< 0.1%
22834.35 40
 
< 0.1%
7103.43 40
 
< 0.1%
31213.73 40
 
< 0.1%
16594.96 40
 
< 0.1%
26636.95 40
 
< 0.1%
20859.61 40
 
< 0.1%
27828.78 40
 
< 0.1%
37585.65 40
 
< 0.1%
Other values (2489) 99560
99.6%
ValueCountFrequency (%)
-15218.08 40
< 0.1%
-11969.03 40
< 0.1%
-9995.26 40
< 0.1%
-9108.36 40
< 0.1%
-8198 40
< 0.1%
-7291.8 40
< 0.1%
-6901.33 40
< 0.1%
-6724.83 40
< 0.1%
-6702.78 40
< 0.1%
-6655.13 40
< 0.1%
ValueCountFrequency (%)
53373.82 40
< 0.1%
51850.94 40
< 0.1%
49394.28 40
< 0.1%
47802.02 40
< 0.1%
47691.14 40
< 0.1%
47577.26 40
< 0.1%
47260.78 40
< 0.1%
46963.45 40
< 0.1%
46853.93 40
< 0.1%
46672.96 40
< 0.1%

num_transactions
Real number (ℝ)

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.9356
Minimum15
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-04-14T22:19:14.324706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile21
Q126
median30
Q334
95-th percentile39
Maximum52
Range37
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.3437569
Coefficient of variation (CV)0.17850843
Kurtosis-0.025579477
Mean29.9356
Median Absolute Deviation (MAD)4
Skewness0.081991953
Sum2993560
Variance28.555738
MonotonicityNot monotonic
2025-04-14T22:19:14.410205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
28 7800
 
7.8%
29 7400
 
7.4%
30 7080
 
7.1%
31 6960
 
7.0%
27 6960
 
7.0%
32 6760
 
6.8%
33 6120
 
6.1%
34 5600
 
5.6%
26 5440
 
5.4%
35 4880
 
4.9%
Other values (24) 35000
35.0%
ValueCountFrequency (%)
15 120
 
0.1%
16 240
 
0.2%
17 400
 
0.4%
18 920
 
0.9%
19 880
 
0.9%
20 1320
 
1.3%
21 1560
 
1.6%
22 2840
2.8%
23 3280
3.3%
24 4080
4.1%
ValueCountFrequency (%)
52 40
 
< 0.1%
49 40
 
< 0.1%
46 80
 
0.1%
45 200
 
0.2%
44 240
 
0.2%
43 640
0.6%
42 520
 
0.5%
41 920
0.9%
40 1200
1.2%
39 1400
1.4%

transaction_amount
Real number (ℝ)

High correlation 

Distinct98406
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19673.189
Minimum50.27
Maximum99986.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-04-14T22:19:14.501995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum50.27
5-th percentile1079.987
Q14767.6475
median9338.06
Q325246.022
95-th percentile75163.58
Maximum99986.39
Range99936.12
Interquartile range (IQR)20478.375

Descriptive statistics

Standard deviation22529.583
Coefficient of variation (CV)1.1451923
Kurtosis2.3807118
Mean19673.189
Median Absolute Deviation (MAD)6901.98
Skewness1.7338031
Sum1.9673189 × 109
Variance5.0758213 × 108
MonotonicityNot monotonic
2025-04-14T22:19:14.595000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6410.59 4
 
< 0.1%
5903.81 3
 
< 0.1%
5422.61 3
 
< 0.1%
3632.84 3
 
< 0.1%
14496.86 3
 
< 0.1%
13085.33 3
 
< 0.1%
2255.91 3
 
< 0.1%
7469.16 3
 
< 0.1%
9975.38 3
 
< 0.1%
3709.57 3
 
< 0.1%
Other values (98396) 99969
> 99.9%
ValueCountFrequency (%)
50.27 1
< 0.1%
50.57 1
< 0.1%
50.78 1
< 0.1%
50.97 1
< 0.1%
50.98 1
< 0.1%
51.12 1
< 0.1%
51.29 1
< 0.1%
51.5 1
< 0.1%
52.21 1
< 0.1%
52.6 1
< 0.1%
ValueCountFrequency (%)
99986.39 1
< 0.1%
99981.98 1
< 0.1%
99979.81 1
< 0.1%
99977.77 1
< 0.1%
99975.67 1
< 0.1%
99962.27 1
< 0.1%
99956.55 1
< 0.1%
99939.32 1
< 0.1%
99933.66 1
< 0.1%
99924.21 1
< 0.1%

transaction_type
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
UPI
20056 
Debit
20046 
Credit
19992 
POS
19963 
ATM
19943 

Length

Max length6
Median length3
Mean length4.00068
Min length3

Characters and Unicode

Total characters400068
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowATM
2nd rowUPI
3rd rowUPI
4th rowATM
5th rowDebit

Common Values

ValueCountFrequency (%)
UPI 20056
20.1%
Debit 20046
20.0%
Credit 19992
20.0%
POS 19963
20.0%
ATM 19943
19.9%

Length

2025-04-14T22:19:14.684131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:19:14.755100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
upi 20056
20.1%
debit 20046
20.0%
credit 19992
20.0%
pos 19963
20.0%
atm 19943
19.9%

Most occurring characters

ValueCountFrequency (%)
e 40038
 
10.0%
i 40038
 
10.0%
t 40038
 
10.0%
P 40019
 
10.0%
U 20056
 
5.0%
I 20056
 
5.0%
D 20046
 
5.0%
b 20046
 
5.0%
C 19992
 
5.0%
r 19992
 
5.0%
Other values (6) 119747
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 400068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 40038
 
10.0%
i 40038
 
10.0%
t 40038
 
10.0%
P 40019
 
10.0%
U 20056
 
5.0%
I 20056
 
5.0%
D 20046
 
5.0%
b 20046
 
5.0%
C 19992
 
5.0%
r 19992
 
5.0%
Other values (6) 119747
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 400068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 40038
 
10.0%
i 40038
 
10.0%
t 40038
 
10.0%
P 40019
 
10.0%
U 20056
 
5.0%
I 20056
 
5.0%
D 20046
 
5.0%
b 20046
 
5.0%
C 19992
 
5.0%
r 19992
 
5.0%
Other values (6) 119747
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 400068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 40038
 
10.0%
i 40038
 
10.0%
t 40038
 
10.0%
P 40019
 
10.0%
U 20056
 
5.0%
I 20056
 
5.0%
D 20046
 
5.0%
b 20046
 
5.0%
C 19992
 
5.0%
r 19992
 
5.0%
Other values (6) 119747
29.9%
Distinct90
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
Minimum2025-01-02 00:00:00
Maximum2025-12-04 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-14T22:19:14.834950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:14.924260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct38007
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
2025-04-14T22:19:15.150392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length12.04348
Min length5

Characters and Unicode

Total characters1204348
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20929 ?
Unique (%)20.9%

Sample

1st rowMoralesfort
2nd rowDeborahburgh
3rd rowSouth Adam
4th rowMarymouth
5th rowLake Susanmouth
ValueCountFrequency (%)
new 7186
 
4.8%
east 7185
 
4.8%
south 7167
 
4.8%
west 7106
 
4.7%
port 7099
 
4.7%
lake 7062
 
4.7%
north 7025
 
4.7%
michael 558
 
0.4%
james 406
 
0.3%
david 391
 
0.3%
Other values (19474) 98645
65.8%
2025-04-14T22:19:15.470522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 116024
 
9.6%
t 95027
 
7.9%
r 93834
 
7.8%
a 93521
 
7.8%
o 82297
 
6.8%
h 67413
 
5.6%
n 65537
 
5.4%
i 59673
 
5.0%
s 56398
 
4.7%
49830
 
4.1%
Other values (43) 424794
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1204348
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 116024
 
9.6%
t 95027
 
7.9%
r 93834
 
7.8%
a 93521
 
7.8%
o 82297
 
6.8%
h 67413
 
5.6%
n 65537
 
5.4%
i 59673
 
5.0%
s 56398
 
4.7%
49830
 
4.1%
Other values (43) 424794
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1204348
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 116024
 
9.6%
t 95027
 
7.9%
r 93834
 
7.8%
a 93521
 
7.8%
o 82297
 
6.8%
h 67413
 
5.6%
n 65537
 
5.4%
i 59673
 
5.0%
s 56398
 
4.7%
49830
 
4.1%
Other values (43) 424794
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1204348
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 116024
 
9.6%
t 95027
 
7.9%
r 93834
 
7.8%
a 93521
 
7.8%
o 82297
 
6.8%
h 67413
 
5.6%
n 65537
 
5.4%
i 59673
 
5.0%
s 56398
 
4.7%
49830
 
4.1%
Other values (43) 424794
35.3%

device_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.9 MiB
Web
33495 
Branch
33288 
Mobile
33217 

Length

Max length6
Median length6
Mean length4.99515
Min length3

Characters and Unicode

Total characters499515
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMobile
2nd rowWeb
3rd rowBranch
4th rowBranch
5th rowBranch

Common Values

ValueCountFrequency (%)
Web 33495
33.5%
Branch 33288
33.3%
Mobile 33217
33.2%

Length

2025-04-14T22:19:15.561739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:19:15.628150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
web 33495
33.5%
branch 33288
33.3%
mobile 33217
33.2%

Most occurring characters

ValueCountFrequency (%)
e 66712
13.4%
b 66712
13.4%
W 33495
 
6.7%
B 33288
 
6.7%
r 33288
 
6.7%
a 33288
 
6.7%
n 33288
 
6.7%
c 33288
 
6.7%
h 33288
 
6.7%
M 33217
 
6.6%
Other values (3) 99651
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 499515
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 66712
13.4%
b 66712
13.4%
W 33495
 
6.7%
B 33288
 
6.7%
r 33288
 
6.7%
a 33288
 
6.7%
n 33288
 
6.7%
c 33288
 
6.7%
h 33288
 
6.7%
M 33217
 
6.6%
Other values (3) 99651
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 499515
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 66712
13.4%
b 66712
13.4%
W 33495
 
6.7%
B 33288
 
6.7%
r 33288
 
6.7%
a 33288
 
6.7%
n 33288
 
6.7%
c 33288
 
6.7%
h 33288
 
6.7%
M 33217
 
6.6%
Other values (3) 99651
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 499515
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 66712
13.4%
b 66712
13.4%
W 33495
 
6.7%
B 33288
 
6.7%
r 33288
 
6.7%
a 33288
 
6.7%
n 33288
 
6.7%
c 33288
 
6.7%
h 33288
 
6.7%
M 33217
 
6.6%
Other values (3) 99651
19.9%

has_credit_card
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
50560 
1
49440 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 50560
50.6%
1 49440
49.4%

Length

2025-04-14T22:19:15.698084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:19:15.759564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 50560
50.6%
1 49440
49.4%

Most occurring characters

ValueCountFrequency (%)
0 50560
50.6%
1 49440
49.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 50560
50.6%
1 49440
49.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 50560
50.6%
1 49440
49.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 50560
50.6%
1 49440
49.4%

loan_approved
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
50600 
1
49400 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 50600
50.6%
1 49400
49.4%

Length

2025-04-14T22:19:15.824178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:19:15.883083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 50600
50.6%
1 49400
49.4%

Most occurring characters

ValueCountFrequency (%)
0 50600
50.6%
1 49400
49.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 50600
50.6%
1 49400
49.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 50600
50.6%
1 49400
49.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 50600
50.6%
1 49400
49.4%

loan_amount
Real number (ℝ)

High correlation  Zeros 

Distinct2364
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192899.86
Minimum0
Maximum493705.74
Zeros3000
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-04-14T22:19:15.954831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43534.696
Q1144999.65
median194831.33
Q3248171.71
95-th percentile317601.55
Maximum493705.74
Range493705.74
Interquartile range (IQR)103172.05

Descriptive statistics

Standard deviation79278.609
Coefficient of variation (CV)0.41098324
Kurtosis0.16434065
Mean192899.86
Median Absolute Deviation (MAD)51583.17
Skewness-0.21495432
Sum1.9289986 × 1010
Variance6.2850979 × 109
MonotonicityNot monotonic
2025-04-14T22:19:16.049087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3000
 
3.0%
194831.325 2520
 
2.5%
237115.06 40
 
< 0.1%
227446.93 40
 
< 0.1%
304148.93 40
 
< 0.1%
259514.88 40
 
< 0.1%
272033.41 40
 
< 0.1%
245999.27 40
 
< 0.1%
156738.55 40
 
< 0.1%
230617.62 40
 
< 0.1%
Other values (2354) 94160
94.2%
ValueCountFrequency (%)
0 3000
3.0%
3362.64 40
 
< 0.1%
4238.66 40
 
< 0.1%
4324.61 40
 
< 0.1%
4366.11 40
 
< 0.1%
5376.96 40
 
< 0.1%
6105.23 40
 
< 0.1%
6460.15 40
 
< 0.1%
9081.01 40
 
< 0.1%
10010.47 40
 
< 0.1%
ValueCountFrequency (%)
493705.74 40
< 0.1%
478715.7 40
< 0.1%
452691.55 40
< 0.1%
450780.72 40
< 0.1%
426378.25 40
< 0.1%
424249.93 40
< 0.1%
417865.54 40
< 0.1%
403457.74 40
< 0.1%
402643.53 40
< 0.1%
400216.87 40
< 0.1%

loan_purpose
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
Business
20680 
Car
20440 
Home
20080 
Education
19720 
Personal
19080 

Length

Max length9
Median length8
Mean length6.372
Min length3

Characters and Unicode

Total characters637200
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersonal
2nd rowPersonal
3rd rowPersonal
4th rowPersonal
5th rowPersonal

Common Values

ValueCountFrequency (%)
Business 20680
20.7%
Car 20440
20.4%
Home 20080
20.1%
Education 19720
19.7%
Personal 19080
19.1%

Length

2025-04-14T22:19:16.137160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:19:16.207148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
business 20680
20.7%
car 20440
20.4%
home 20080
20.1%
education 19720
19.7%
personal 19080
19.1%

Most occurring characters

ValueCountFrequency (%)
s 81120
12.7%
e 59840
 
9.4%
n 59480
 
9.3%
a 59240
 
9.3%
o 58880
 
9.2%
i 40400
 
6.3%
u 40400
 
6.3%
r 39520
 
6.2%
B 20680
 
3.2%
C 20440
 
3.2%
Other values (8) 157200
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 637200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 81120
12.7%
e 59840
 
9.4%
n 59480
 
9.3%
a 59240
 
9.3%
o 58880
 
9.2%
i 40400
 
6.3%
u 40400
 
6.3%
r 39520
 
6.2%
B 20680
 
3.2%
C 20440
 
3.2%
Other values (8) 157200
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 637200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 81120
12.7%
e 59840
 
9.4%
n 59480
 
9.3%
a 59240
 
9.3%
o 58880
 
9.2%
i 40400
 
6.3%
u 40400
 
6.3%
r 39520
 
6.2%
B 20680
 
3.2%
C 20440
 
3.2%
Other values (8) 157200
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 637200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 81120
12.7%
e 59840
 
9.4%
n 59480
 
9.3%
a 59240
 
9.3%
o 58880
 
9.2%
i 40400
 
6.3%
u 40400
 
6.3%
r 39520
 
6.2%
B 20680
 
3.2%
C 20440
 
3.2%
Other values (8) 157200
24.7%

credit_utilization
Real number (ℝ)

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.490656
Minimum0
Maximum1
Zeros480
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-04-14T22:19:16.292859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q10.24
median0.49
Q30.73
95-th percentile0.94
Maximum1
Range1
Interquartile range (IQR)0.49

Descriptive statistics

Standard deviation0.28520348
Coefficient of variation (CV)0.58126972
Kurtosis-1.162722
Mean0.490656
Median Absolute Deviation (MAD)0.25
Skewness0.042016701
Sum49065.6
Variance0.081341023
MonotonicityNot monotonic
2025-04-14T22:19:16.386294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.21 1480
 
1.5%
0.49 1440
 
1.4%
0.61 1360
 
1.4%
0.1 1320
 
1.3%
0.57 1320
 
1.3%
0.47 1280
 
1.3%
0.26 1280
 
1.3%
0.54 1280
 
1.3%
0.85 1280
 
1.3%
0.6 1280
 
1.3%
Other values (91) 86680
86.7%
ValueCountFrequency (%)
0 480
 
0.5%
0.01 1040
1.0%
0.02 880
0.9%
0.03 1080
1.1%
0.04 960
1.0%
0.05 1240
1.2%
0.06 1200
1.2%
0.07 800
0.8%
0.08 1000
1.0%
0.09 840
0.8%
ValueCountFrequency (%)
1 400
 
0.4%
0.99 680
0.7%
0.98 1040
1.0%
0.97 880
0.9%
0.96 800
0.8%
0.95 880
0.9%
0.94 960
1.0%
0.93 1120
1.1%
0.92 1160
1.2%
0.91 1080
1.1%

default_history
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
1
50760 
0
49240 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 50760
50.8%
0 49240
49.2%

Length

2025-04-14T22:19:16.467945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:19:16.526630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50760
50.8%
0 49240
49.2%

Most occurring characters

ValueCountFrequency (%)
1 50760
50.8%
0 49240
49.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 50760
50.8%
0 49240
49.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 50760
50.8%
0 49240
49.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 50760
50.8%
0 49240
49.2%

branch_rating
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
4
21120 
5
20720 
2
20640 
3
18880 
1
18640 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
4 21120
21.1%
5 20720
20.7%
2 20640
20.6%
3 18880
18.9%
1 18640
18.6%

Length

2025-04-14T22:19:16.590562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:19:16.656489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4 21120
21.1%
5 20720
20.7%
2 20640
20.6%
3 18880
18.9%
1 18640
18.6%

Most occurring characters

ValueCountFrequency (%)
4 21120
21.1%
5 20720
20.7%
2 20640
20.6%
3 18880
18.9%
1 18640
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 21120
21.1%
5 20720
20.7%
2 20640
20.6%
3 18880
18.9%
1 18640
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 21120
21.1%
5 20720
20.7%
2 20640
20.6%
3 18880
18.9%
1 18640
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 21120
21.1%
5 20720
20.7%
2 20640
20.6%
3 18880
18.9%
1 18640
18.6%

potential_data_leakage
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
0
52160 
1
47840 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 52160
52.2%
1 47840
47.8%

Length

2025-04-14T22:19:16.731462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:19:16.790203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 52160
52.2%
1 47840
47.8%

Most occurring characters

ValueCountFrequency (%)
0 52160
52.2%
1 47840
47.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 52160
52.2%
1 47840
47.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 52160
52.2%
1 47840
47.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 52160
52.2%
1 47840
47.8%

pca_1
Real number (ℝ)

High correlation 

Distinct2500
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.1368684 × 10-18
Minimum-3.6178642
Maximum3.546399
Zeros0
Zeros (%)0.0%
Negative49480
Negative (%)49.5%
Memory size781.4 KiB
2025-04-14T22:19:16.857265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-3.6178642
5-th percentile-1.6965701
Q1-0.69716289
median0.0083633478
Q30.69458212
95-th percentile1.6739771
Maximum3.546399
Range7.1642631
Interquartile range (IQR)1.391745

Descriptive statistics

Standard deviation1.0258463
Coefficient of variation (CV)-9.0234395 × 1017
Kurtosis0.1138397
Mean-1.1368684 × 10-18
Median Absolute Deviation (MAD)0.69628493
Skewness0.015404185
Sum1.2505552 × 10-12
Variance1.0523606
MonotonicityNot monotonic
2025-04-14T22:19:16.947326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.2041517771 40
 
< 0.1%
0.02187843649 40
 
< 0.1%
-0.6499245679 40
 
< 0.1%
-0.5115197416 40
 
< 0.1%
2.062301976 40
 
< 0.1%
0.7071842468 40
 
< 0.1%
1.170570834 40
 
< 0.1%
-0.1870268049 40
 
< 0.1%
1.424886461 40
 
< 0.1%
0.5010734353 40
 
< 0.1%
Other values (2490) 99600
99.6%
ValueCountFrequency (%)
-3.617864168 40
< 0.1%
-3.465897028 40
< 0.1%
-3.318677398 40
< 0.1%
-3.203900036 40
< 0.1%
-2.95564782 40
< 0.1%
-2.836249772 40
< 0.1%
-2.811685225 40
< 0.1%
-2.770784522 40
< 0.1%
-2.727102914 40
< 0.1%
-2.725494672 40
< 0.1%
ValueCountFrequency (%)
3.546398975 40
< 0.1%
3.432779605 40
< 0.1%
3.276167244 40
< 0.1%
3.246916211 40
< 0.1%
3.152939047 40
< 0.1%
3.105105046 40
< 0.1%
3.09636372 40
< 0.1%
2.947964777 40
< 0.1%
2.941294121 40
< 0.1%
2.901912694 40
< 0.1%

pca_2
Real number (ℝ)

High correlation 

Distinct2500
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5011104 × 10-17
Minimum-3.3042718
Maximum3.1381052
Zeros0
Zeros (%)0.0%
Negative49080
Negative (%)49.1%
Memory size781.4 KiB
2025-04-14T22:19:17.035160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-3.3042718
5-th percentile-1.7711657
Q1-0.66635621
median0.033505931
Q30.66684176
95-th percentile1.6164746
Maximum3.1381052
Range6.442377
Interquartile range (IQR)1.333198

Descriptive statistics

Standard deviation1.00887
Coefficient of variation (CV)4.0336885 × 1016
Kurtosis0.10297923
Mean2.5011104 × 10-17
Median Absolute Deviation (MAD)0.66102645
Skewness-0.12943649
Sum1.2505552 × 10-12
Variance1.0178188
MonotonicityNot monotonic
2025-04-14T22:19:17.123319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8267322674 40
 
< 0.1%
0.4821690638 40
 
< 0.1%
1.737255526 40
 
< 0.1%
0.4520600605 40
 
< 0.1%
0.06165941181 40
 
< 0.1%
-0.9445612927 40
 
< 0.1%
0.9102230024 40
 
< 0.1%
0.04845390424 40
 
< 0.1%
0.9318174168 40
 
< 0.1%
-0.3472561166 40
 
< 0.1%
Other values (2490) 99600
99.6%
ValueCountFrequency (%)
-3.304271813 40
< 0.1%
-3.198447758 40
< 0.1%
-3.190631893 40
< 0.1%
-3.080113133 40
< 0.1%
-3.075595029 40
< 0.1%
-2.94397706 40
< 0.1%
-2.918619623 40
< 0.1%
-2.909682574 40
< 0.1%
-2.902263913 40
< 0.1%
-2.881619913 40
< 0.1%
ValueCountFrequency (%)
3.138105178 40
< 0.1%
3.074965874 40
< 0.1%
2.983873978 40
< 0.1%
2.965653142 40
< 0.1%
2.950217337 40
< 0.1%
2.873848536 40
< 0.1%
2.870980718 40
< 0.1%
2.705246691 40
< 0.1%
2.655423754 40
< 0.1%
2.60107966 40
< 0.1%

cluster
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
1
27200 
2
26800 
3
24360 
0
21640 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 27200
27.2%
2 26800
26.8%
3 24360
24.4%
0 21640
21.6%

Length

2025-04-14T22:19:17.206059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:19:17.268839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 27200
27.2%
2 26800
26.8%
3 24360
24.4%
0 21640
21.6%

Most occurring characters

ValueCountFrequency (%)
1 27200
27.2%
2 26800
26.8%
3 24360
24.4%
0 21640
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 27200
27.2%
2 26800
26.8%
3 24360
24.4%
0 21640
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 27200
27.2%
2 26800
26.8%
3 24360
24.4%
0 21640
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 27200
27.2%
2 26800
26.8%
3 24360
24.4%
0 21640
21.6%

income_log
Real number (ℝ)

High correlation 

Distinct2370
Distinct (%)2.4%
Missing200
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean10.7678
Minimum3.6689318
Maximum11.577433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-04-14T22:19:17.346603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.6689318
5-th percentile10.150928
Q110.604605
median10.821286
Q311.003189
95-th percentile11.215611
Maximum11.577433
Range7.9085011
Interquartile range (IQR)0.3985832

Descriptive statistics

Standard deviation0.36769558
Coefficient of variation (CV)0.034147697
Kurtosis56.897823
Mean10.7678
Median Absolute Deviation (MAD)0.19398681
Skewness-3.8171406
Sum1074626.5
Variance0.13520004
MonotonicityNot monotonic
2025-04-14T22:19:17.631059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.82128605 5040
 
5.0%
10.57097995 40
 
< 0.1%
11.01434243 40
 
< 0.1%
11.10624827 40
 
< 0.1%
10.76437339 40
 
< 0.1%
10.75338973 40
 
< 0.1%
10.81120363 40
 
< 0.1%
10.32226696 40
 
< 0.1%
10.86965757 40
 
< 0.1%
11.06519263 40
 
< 0.1%
Other values (2360) 94400
94.4%
(Missing) 200
 
0.2%
ValueCountFrequency (%)
3.668931816 40
< 0.1%
8.463011519 40
< 0.1%
9.189303643 40
< 0.1%
9.229020801 40
< 0.1%
9.239347566 40
< 0.1%
9.244125184 40
< 0.1%
9.267630499 40
< 0.1%
9.27787191 40
< 0.1%
9.329909367 40
< 0.1%
9.356579553 40
< 0.1%
ValueCountFrequency (%)
11.57743289 40
< 0.1%
11.54585596 40
< 0.1%
11.53962718 40
< 0.1%
11.49728824 40
< 0.1%
11.49517088 40
< 0.1%
11.48488035 40
< 0.1%
11.47936641 40
< 0.1%
11.4514821 40
< 0.1%
11.44845617 40
< 0.1%
11.44487422 40
< 0.1%

loan_log
Real number (ℝ)

High correlation  Zeros 

Distinct2364
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.745456
Minimum0
Maximum13.109697
Zeros3000
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-04-14T22:19:17.724298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.68133
Q111.884493
median12.179895
Q312.42188
95-th percentile12.668556
Maximum13.109697
Range13.109697
Interquartile range (IQR)0.53738676

Descriptive statistics

Standard deviation2.1218612
Coefficient of variation (CV)0.18065379
Kurtosis25.198248
Mean11.745456
Median Absolute Deviation (MAD)0.26015095
Skewness-5.0852944
Sum1174545.6
Variance4.5022948
MonotonicityNot monotonic
2025-04-14T22:19:17.818630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3000
 
3.0%
12.1798946 2520
 
2.5%
12.376305 40
 
< 0.1%
12.33467661 40
 
< 0.1%
12.62527605 40
 
< 0.1%
12.46657317 40
 
< 0.1%
12.51368384 40
 
< 0.1%
12.41308791 40
 
< 0.1%
11.96234079 40
 
< 0.1%
12.34852063 40
 
< 0.1%
Other values (2354) 94160
94.2%
ValueCountFrequency (%)
0 3000
3.0%
8.120779 40
 
< 0.1%
8.352238356 40
 
< 0.1%
8.37230845 40
 
< 0.1%
8.381856742 40
 
< 0.1%
8.590064399 40
 
< 0.1%
8.71706484 40
 
< 0.1%
8.7735626 40
 
< 0.1%
9.114050813 40
 
< 0.1%
9.211486715 40
 
< 0.1%
ValueCountFrequency (%)
13.10969698 40
< 0.1%
13.07886426 40
< 0.1%
13.02296848 40
< 0.1%
13.01873851 40
< 0.1%
12.96308449 40
< 0.1%
12.95808038 40
< 0.1%
12.94291738 40
< 0.1%
12.90782951 40
< 0.1%
12.90580939 40
< 0.1%
12.89976435 40
< 0.1%

month
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size781.4 KiB

income_credit_interaction
Real number (ℝ)

High correlation 

Distinct2459
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34016077
Minimum-8555859
Maximum72239462
Zeros0
Zeros (%)0.0%
Negative200
Negative (%)0.2%
Memory size781.4 KiB
2025-04-14T22:19:17.907771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-8555859
5-th percentile16788952
Q127231788
median34050623
Q340945759
95-th percentile51241228
Maximum72239462
Range80795321
Interquartile range (IQR)13713971

Descriptive statistics

Standard deviation10534197
Coefficient of variation (CV)0.30968289
Kurtosis0.25726617
Mean34016077
Median Absolute Deviation (MAD)6848024.1
Skewness0.01471746
Sum3.4016077 × 1012
Variance1.1096931 × 1014
MonotonicityNot monotonic
2025-04-14T22:19:18.003735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34050622.6 360
 
0.4%
34401143.71 160
 
0.2%
30745709.23 120
 
0.1%
35402632.62 120
 
0.1%
33049133.7 120
 
0.1%
32848835.92 120
 
0.1%
32598463.7 120
 
0.1%
29744220.33 80
 
0.1%
31446751.46 80
 
0.1%
29794294.77 80
 
0.1%
Other values (2449) 98640
98.6%
ValueCountFrequency (%)
-8555859 40
< 0.1%
-6346967.94 40
< 0.1%
-2696041.68 40
< 0.1%
-1209817.94 40
< 0.1%
-546559.52 40
< 0.1%
26059.22 40
< 0.1%
3627239.8 40
< 0.1%
6464204.96 40
< 0.1%
6657764.4 40
< 0.1%
6723789.6 40
< 0.1%
ValueCountFrequency (%)
72239462.13 40
< 0.1%
70008084 40
< 0.1%
69839495.2 40
< 0.1%
69306997.76 40
< 0.1%
68903878.1 40
< 0.1%
68669207.79 40
< 0.1%
65253821.04 40
< 0.1%
64161891.69 40
< 0.1%
63861822.22 40
< 0.1%
63700325.4 40
< 0.1%

credit_to_income
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct2422
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9815238
Minimum-146.93555
Maximum4421.2385
Zeros3000
Zeros (%)3.0%
Negative200
Negative (%)0.2%
Memory size781.4 KiB
2025-04-14T22:19:18.095157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-146.93555
5-th percentile0.76716213
Q12.7351999
median3.9170454
Q35.3352779
95-th percentile9.0055343
Maximum4421.2385
Range4568.174
Interquartile range (IQR)2.600078

Descriptive statistics

Standard deviation88.467631
Coefficient of variation (CV)14.790149
Kurtosis2478.8622
Mean5.9815238
Median Absolute Deviation (MAD)1.2778802
Skewness49.723255
Sum598152.38
Variance7826.5217
MonotonicityNot monotonic
2025-04-14T22:19:18.182672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3000
 
3.0%
3.890755739 200
 
0.2%
4.139720874 40
 
< 0.1%
5.342704007 40
 
< 0.1%
7.481290108 40
 
< 0.1%
5.007467681 40
 
< 0.1%
4.937759824 40
 
< 0.1%
2.76275997 40
 
< 0.1%
6.069575793 40
 
< 0.1%
2.290108463 40
 
< 0.1%
Other values (2412) 96480
96.5%
ValueCountFrequency (%)
-146.9355479 40
 
< 0.1%
-120.8173668 40
 
< 0.1%
-46.40389929 40
 
< 0.1%
-17.87461673 40
 
< 0.1%
-10.59075407 40
 
< 0.1%
0 3000
3.0%
0.05693434884 40
 
< 0.1%
0.06587706707 40
 
< 0.1%
0.06967629903 40
 
< 0.1%
0.09076292571 40
 
< 0.1%
ValueCountFrequency (%)
4421.23846 40
< 0.1%
59.49816312 40
< 0.1%
33.67693169 40
< 0.1%
31.2933496 40
< 0.1%
24.23378343 40
< 0.1%
23.76321732 40
< 0.1%
22.0952875 40
< 0.1%
21.82118979 40
< 0.1%
20.94962392 40
< 0.1%
20.60981454 40
< 0.1%

txn_intensity
Real number (ℝ)

High correlation  Skewed 

Distinct2500
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0011140181
Minimum-5.9649123
Maximum3.4414946
Zeros0
Zeros (%)0.0%
Negative2160
Negative (%)2.2%
Memory size781.4 KiB
2025-04-14T22:19:18.268968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-5.9649123
5-th percentile0.0006632706
Q10.001049692
median0.001466099
Q30.0021517368
95-th percentile0.0052566921
Maximum3.4414946
Range9.4064069
Interquartile range (IQR)0.0011020448

Descriptive statistics

Standard deviation0.13934555
Coefficient of variation (CV)125.08373
Kurtosis1490.2082
Mean0.0011140181
Median Absolute Deviation (MAD)0.00048570727
Skewness-25.322341
Sum111.40181
Variance0.019417181
MonotonicityNot monotonic
2025-04-14T22:19:18.357005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002446114767 40
 
< 0.1%
0.001357544334 40
 
< 0.1%
0.001462635157 40
 
< 0.1%
0.003659688392 40
 
< 0.1%
0.001153301662 40
 
< 0.1%
0.001807668854 40
 
< 0.1%
0.001388995775 40
 
< 0.1%
0.001486054339 40
 
< 0.1%
0.001185780125 40
 
< 0.1%
0.0009577868738 40
 
< 0.1%
Other values (2490) 99600
99.6%
ValueCountFrequency (%)
-5.964912281 40
< 0.1%
-0.4202972834 40
< 0.1%
-0.3215051203 40
< 0.1%
-0.1018564153 40
< 0.1%
-0.07884761183 40
< 0.1%
-0.07525235488 40
< 0.1%
-0.07378258731 40
< 0.1%
-0.0690157586 40
< 0.1%
-0.04340939755 40
< 0.1%
-0.03836071862 40
< 0.1%
ValueCountFrequency (%)
3.441494592 40
< 0.1%
0.7416825599 40
< 0.1%
0.3455889412 40
< 0.1%
0.193143409 40
< 0.1%
0.1467812959 40
< 0.1%
0.09756561883 40
< 0.1%
0.09644701636 40
< 0.1%
0.07740324594 40
< 0.1%
0.07547961002 40
< 0.1%
0.06813589587 40
< 0.1%

cluster_label
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
1
27200 
2
26800 
3
24360 
0
21640 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 27200
27.2%
2 26800
26.8%
3 24360
24.4%
0 21640
21.6%

Length

2025-04-14T22:19:18.435624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:19:18.497813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 27200
27.2%
2 26800
26.8%
3 24360
24.4%
0 21640
21.6%

Most occurring characters

ValueCountFrequency (%)
1 27200
27.2%
2 26800
26.8%
3 24360
24.4%
0 21640
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 27200
27.2%
2 26800
26.8%
3 24360
24.4%
0 21640
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 27200
27.2%
2 26800
26.8%
3 24360
24.4%
0 21640
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 27200
27.2%
2 26800
26.8%
3 24360
24.4%
0 21640
21.6%

Interactions

2025-04-14T22:19:10.692003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:57.770007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:58.724420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:59.689552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:00.764086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:01.777328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:02.833487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:03.773281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:04.832225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:05.769109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:06.708065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:07.678082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:08.743287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:09.739990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:10.755791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:57.834471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:58.790895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:59.757197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:00.847382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:01.843631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:02.898203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:03.837893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:04.896285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:05.835399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:06.773279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:07.743468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:08.811970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:09.803111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:10.821257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:57.903445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:58.860019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:59.905919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:00.920838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:01.913307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:02.966547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:03.908060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:04.965650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:05.903186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:06.845389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:07.941701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:08.884107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:09.869929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:10.887893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:57.971682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:58.929707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:59.976089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:00.991657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:02.075980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:03.034793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:03.976587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:05.033133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:05.971668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:06.916426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:08.011076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:08.958615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:09.935837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:10.956177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:58.042695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:59.002675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:00.050064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:01.064776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:02.148929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:03.105715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:04.048258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:05.104091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:06.043255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:06.991118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:08.081789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:09.035757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:10.006863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:11.020798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:58.106417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:59.070256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:00.118626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:01.134415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:02.215494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:03.172413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:04.114460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:05.170096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:06.109054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:07.060233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:08.147040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:09.105735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:10.070665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:11.087288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:58.174394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:59.138793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:00.188008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:01.204347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:02.283541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:03.238122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:04.181670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:05.235389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:06.173975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:07.128863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:08.211622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:09.175486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:10.134670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:11.152076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:58.242577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:59.205248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:00.256834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:01.272602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:02.350946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:03.303839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:04.245930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:05.301498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:06.240534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:07.196974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:08.277623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:09.245656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:10.198689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:11.216734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:58.314670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:59.274394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:00.330086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:01.347337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:02.418931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:03.371791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:04.313724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:05.368160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:06.306702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:07.266792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:08.344597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:09.316863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:10.263301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:11.281500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:58.385455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:59.342997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:00.400967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:01.422036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:02.487611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:03.438787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:04.379129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:05.434725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:06.373245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:07.334402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:08.410281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:09.388194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:10.329207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:11.349107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:58.456664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:59.415586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:00.479739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:01.495700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:02.560977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:03.508484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:04.449379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:05.503536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:06.443918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:07.404898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:08.480534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:09.460651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:10.396716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:11.413852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:58.524840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:59.482933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:00.552874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:01.566828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:02.630460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:03.573608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:04.515607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:05.570264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:06.509822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:07.473914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:08.546197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:09.529474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:10.461861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:11.483894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:58.599276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:59.559630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:00.629317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:01.642947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:02.705220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:03.647495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:04.703656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:05.643501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:06.582580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:07.548375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:08.618596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:09.606340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:10.569340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:11.545196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:58.661041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:18:59.625087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:00.694789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:01.708146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:02.768735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:03.709435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:04.766928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:05.706341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:06.645277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:07.613767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:08.680479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:09.672980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:19:10.630529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-04-14T22:19:18.572770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
account_typeavg_monthly_balancebranch_ratingclustercluster_labelcredit_scorecredit_to_incomecredit_utilizationdefault_historydevice_typeemployment_statushas_credit_cardincomeincome_credit_interactionincome_logloan_amountloan_approvedloan_logloan_purposenum_transactionspca_1pca_2potential_data_leakagerisk_segmenttransaction_amounttransaction_typetxn_intensity
account_type1.0000.0510.0470.0440.0440.0320.0290.0690.0220.0020.0240.0090.0500.0630.0430.0440.0470.0340.0420.0590.0420.0530.0590.0400.0050.0000.037
avg_monthly_balance0.0511.0000.0590.3820.382-0.0070.0050.0260.0970.0050.0500.0780.0350.0320.0350.0340.0680.0340.0570.0010.6240.0380.0590.046-0.0020.000-0.805
branch_rating0.0470.0591.0000.0410.0410.0580.0390.0580.0330.0050.0400.0360.0530.0460.0390.0540.0130.0460.0440.0480.0510.0680.0240.0600.0030.0000.038
cluster0.0440.3820.0411.0001.0000.3820.0350.0680.0190.0050.0270.0150.2960.2660.2440.4180.0710.3910.0300.0640.4030.4490.0840.0280.0030.0030.045
cluster_label0.0440.3820.0411.0001.0000.3820.0350.0680.0190.0050.0270.0150.2960.2660.2440.4180.0710.3910.0300.0640.4030.4490.0840.0280.0030.0030.045
credit_score0.032-0.0070.0580.3820.3821.0000.007-0.0050.0610.0000.0510.0880.0320.2520.0310.0160.0410.0160.0600.0040.4220.0190.0490.057-0.0020.0000.007
credit_to_income0.0290.0050.0390.0350.0350.0071.000-0.0230.0200.0000.0280.019-0.566-0.545-0.5750.7680.0190.7680.0390.0060.0200.9750.0200.028-0.0050.000-0.010
credit_utilization0.0690.0260.0580.0680.068-0.005-0.0231.0000.0770.0000.0670.0710.0070.0010.007-0.0220.070-0.0220.051-0.0370.013-0.0180.0710.050-0.0040.002-0.016
default_history0.0220.0970.0330.0190.0190.0610.0200.0771.0000.0040.0240.0260.0450.0250.0340.0490.0320.0210.0280.0590.0520.0450.0270.0360.0030.0000.034
device_type0.0020.0050.0050.0050.0050.0000.0000.0000.0041.0000.0030.0010.0030.0020.0000.0000.0000.0010.0020.0000.0000.0000.0000.0000.0030.0050.000
employment_status0.0240.0500.0400.0270.0270.0510.0280.0670.0240.0031.0000.0130.0720.0730.0510.0530.0270.0360.0340.0560.0410.0610.0330.0280.0000.0000.037
has_credit_card0.0090.0780.0360.0150.0150.0880.0190.0710.0260.0010.0131.0000.0340.0590.0250.0760.0280.0410.0400.0520.0610.0480.0180.0150.0000.0040.034
income0.0500.0350.0530.2960.2960.032-0.5660.0070.0450.0030.0720.0341.0000.9681.000-0.0060.079-0.0060.058-0.0080.505-0.6400.0740.0330.0030.003-0.020
income_credit_interaction0.0630.0320.0460.2660.2660.252-0.5450.0010.0250.0020.0730.0590.9681.0000.968-0.0020.057-0.0020.055-0.0070.590-0.6130.0570.0460.0020.000-0.016
income_log0.0430.0350.0390.2440.2440.031-0.5750.0070.0340.0000.0510.0251.0000.9681.000-0.0070.045-0.0070.044-0.0070.502-0.6380.0450.0370.0020.000-0.020
loan_amount0.0440.0340.0540.4180.4180.0160.768-0.0220.0490.0000.0530.076-0.006-0.002-0.0071.0000.1261.0000.0500.0010.3920.7340.1670.073-0.0060.002-0.024
loan_approved0.0470.0680.0130.0710.0710.0410.0190.0700.0320.0000.0270.0280.0790.0570.0450.1261.0000.1520.0380.0490.0600.0720.9460.0040.0100.0070.034
loan_log0.0340.0340.0460.3910.3910.0160.768-0.0220.0210.0010.0360.041-0.006-0.002-0.0071.0000.1521.0000.0420.0010.3920.7340.1740.036-0.0060.000-0.024
loan_purpose0.0420.0570.0440.0300.0300.0600.0390.0510.0280.0020.0340.0400.0580.0550.0440.0500.0380.0421.0000.0670.0560.0530.0260.0330.0030.0000.038
num_transactions0.0590.0010.0480.0640.0640.0040.006-0.0370.0590.0000.0560.052-0.008-0.007-0.0070.0010.0490.0010.0671.000-0.0040.0080.0480.049-0.0050.0000.326
pca_10.0420.6240.0510.4030.4030.4220.0200.0130.0520.0000.0410.0610.5050.5900.5020.3920.0600.3920.056-0.0041.000-0.0050.0610.045-0.0030.004-0.478
pca_20.0530.0380.0680.4490.4490.0190.975-0.0180.0450.0000.0610.048-0.640-0.613-0.6380.7340.0720.7340.0530.008-0.0051.0000.1160.057-0.0060.005-0.033
potential_data_leakage0.0590.0590.0240.0840.0840.0490.0200.0710.0270.0000.0330.0180.0740.0570.0450.1670.9460.1740.0260.0480.0610.1161.0000.0040.0090.0050.034
risk_segment0.0400.0460.0600.0280.0280.0570.0280.0500.0360.0000.0280.0150.0330.0460.0370.0730.0040.0360.0330.0490.0450.0570.0041.0000.0000.0040.037
transaction_amount0.005-0.0020.0030.0030.003-0.002-0.005-0.0040.0030.0030.0000.0000.0030.0020.002-0.0060.010-0.0060.003-0.005-0.003-0.0060.0090.0001.0000.535-0.002
transaction_type0.0000.0000.0000.0030.0030.0000.0000.0020.0000.0050.0000.0040.0030.0000.0000.0020.0070.0000.0000.0000.0040.0050.0050.0040.5351.0000.002
txn_intensity0.037-0.8050.0380.0450.0450.007-0.010-0.0160.0340.0000.0370.034-0.020-0.016-0.020-0.0240.034-0.0240.0380.326-0.478-0.0330.0340.037-0.0020.0021.000

Missing values

2025-04-14T22:19:11.834735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-14T22:19:12.255706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idaccount_typeincomecredit_scoreemployment_statusrisk_segmentavg_monthly_balancenum_transactionstransaction_amounttransaction_typetransaction_datetxn_locationdevice_typehas_credit_cardloan_approvedloan_amountloan_purposecredit_utilizationdefault_historybranch_ratingpotential_data_leakagepca_1pca_2clusterincome_logloan_logmonthincome_credit_interactioncredit_to_incometxn_intensitycluster_label
0CUST00001Checking38985.86727.0Self-employedMedium13080.97324049.11ATM2025-01-14MoralesfortMobile11230617.62Personal0.92120-0.2041520.826732210.5709812.3485212025-0128342720.225.9152650.0024462
1CUST00001Checking38985.86727.0Self-employedMedium13080.97323626.64UPI2025-01-17DeborahburghWeb11230617.62Personal0.92120-0.2041520.826732210.5709812.3485212025-0128342720.225.9152650.0024462
2CUST00001Checking38985.86727.0Self-employedMedium13080.97327761.47UPI2025-01-18South AdamBranch11230617.62Personal0.92120-0.2041520.826732210.5709812.3485212025-0128342720.225.9152650.0024462
3CUST00001Checking38985.86727.0Self-employedMedium13080.97326930.75ATM2025-01-19MarymouthBranch11230617.62Personal0.92120-0.2041520.826732210.5709812.3485212025-0128342720.225.9152650.0024462
4CUST00001Checking38985.86727.0Self-employedMedium13080.973219373.61Debit2025-01-20Lake SusanmouthBranch11230617.62Personal0.92120-0.2041520.826732210.5709812.3485212025-0128342720.225.9152650.0024462
5CUST00001Checking38985.86727.0Self-employedMedium13080.97327150.20UPI2025-01-20KellyboroughMobile11230617.62Personal0.92120-0.2041520.826732210.5709812.3485212025-0128342720.225.9152650.0024462
6CUST00001Checking38985.86727.0Self-employedMedium13080.97321995.91ATM2025-01-20South RogertonMobile11230617.62Personal0.92120-0.2041520.826732210.5709812.3485212025-0128342720.225.9152650.0024462
7CUST00001Checking38985.86727.0Self-employedMedium13080.97328947.42POS2025-01-20West RoymouthWeb11230617.62Personal0.92120-0.2041520.826732210.5709812.3485212025-0128342720.225.9152650.0024462
8CUST00001Checking38985.86727.0Self-employedMedium13080.9732705.97UPI2025-01-22MartinstadBranch11230617.62Personal0.92120-0.2041520.826732210.5709812.3485212025-0128342720.225.9152650.0024462
9CUST00001Checking38985.86727.0Self-employedMedium13080.973266056.37Credit2025-01-25Lake NicholaschesterWeb11230617.62Personal0.92120-0.2041520.826732210.5709812.3485212025-0128342720.225.9152650.0024462
customer_idaccount_typeincomecredit_scoreemployment_statusrisk_segmentavg_monthly_balancenum_transactionstransaction_amounttransaction_typetransaction_datetxn_locationdevice_typehas_credit_cardloan_approvedloan_amountloan_purposecredit_utilizationdefault_historybranch_ratingpotential_data_leakagepca_1pca_2clusterincome_logloan_logmonthincome_credit_interactioncredit_to_incometxn_intensitycluster_label
99990CUST09998Salary62401.16755.0EmployedLow6957.692911081.77ATM2025-03-21StewartfurtBranch1042718.3Home0.02041-0.485082-2.000292011.04135510.6624062025-0347112875.80.6845640.0041670
99991CUST09998Salary62401.16755.0EmployedLow6957.69292508.48POS2025-03-22North ChristopherWeb1042718.3Home0.02041-0.485082-2.000292011.04135510.6624062025-0347112875.80.6845640.0041670
99992CUST09998Salary62401.16755.0EmployedLow6957.69298548.16UPI2025-03-23South ThomasburghBranch1042718.3Home0.02041-0.485082-2.000292011.04135510.6624062025-0347112875.80.6845640.0041670
99993CUST09998Salary62401.16755.0EmployedLow6957.692953260.52Credit2025-03-24JonessideBranch1042718.3Home0.02041-0.485082-2.000292011.04135510.6624062025-0347112875.80.6845640.0041670
99994CUST09998Salary62401.16755.0EmployedLow6957.69297456.73UPI2025-03-27Port TracyportBranch1042718.3Home0.02041-0.485082-2.000292011.04135510.6624062025-0347112875.80.6845640.0041670
99995CUST09998Salary62401.16755.0EmployedLow6957.69299016.51ATM2025-03-29MadisonburghBranch1042718.3Home0.02041-0.485082-2.000292011.04135510.6624062025-0347112875.80.6845640.0041670
99996CUST09998Salary62401.16755.0EmployedLow6957.69299217.11POS2025-03-04DanachesterMobile1042718.3Home0.02041-0.485082-2.000292011.04135510.6624062025-0347112875.80.6845640.0041670
99997CUST09998Salary62401.16755.0EmployedLow6957.692913560.13ATM2025-05-04New DeannaMobile1042718.3Home0.02041-0.485082-2.000292011.04135510.6624062025-0547112875.80.6845640.0041670
99998CUST09998Salary62401.16755.0EmployedLow6957.69294030.95UPI2025-07-04New MaryMobile1042718.3Home0.02041-0.485082-2.000292011.04135510.6624062025-0747112875.80.6845640.0041670
99999CUST09998Salary62401.16755.0EmployedLow6957.6929792.06UPI2025-07-04West GaryfurtBranch1042718.3Home0.02041-0.485082-2.000292011.04135510.6624062025-0747112875.80.6845640.0041670